Bibliographic record
Abstract
Abstract Women’s movements have played a crucial role in fighting for women’s rights and freedoms. Some women join the armed movements in search of equality. Women have participated in grassroots movements demanding space in the political arena. For example, postwar countries like Burundi, Nepal, Sri Lanka and Rwanda have effectively passed women’s quota seats (as part of a peace deal) thanks to women’s movements, and this has helped women to enter politics. Despite the formal progress in descriptive representation, women politicians face backlash. Feminist scholars argue that the rise of anti-feminist values threatens women’s gains. Building on this argument, this study investigates how female politicians responded to patriarchy in postwar Nepal. It asks how women who enter political spaces navigate patriarchy while sustaining their political positions and power. The paper's findings offer three distinct categories of female politicians (risk-takers, opportunity seekers, and opt-to-disengage). Categorization unpacks diverse strategies and tactics women politicians developed to respond to patriarchy, retain positions and power, and make their current and future political and personal decisions. The study relies on thirty-one in-depth interviews conducted with women politicians. This paper enhances existing debates on patriarchy and women politicians and an understanding of quota politics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".